Replicating Online Yankee Auctions to Analyze Auctioneers' and Bidders' Strategies

Dynamic price setting mechanisms such as online auctions typify the new generation of mercantile processes being used on the WWW. However, arbitrary pricing mechanisms can result in loss of revenue and social capital in markets characterized by tight margins. This work presents a relatively risk-free and cost-effective approach to managing innovation in the area of web-based dynamic price setting processes. We focus on Yankee auctions, which sells multiple identical units of a good to multiple buyers using an ascending and open auction mechanism. This mechanism has its roots in the traditional English auction; however, significant new rules make it an interesting mechanism to study. This study presents a multi-agent simulation approach to manage the optimization of sellers’ revenue. It is based on the theoretical revenue generating properties of the Yankee auctions and utilizes data from real auctions to instantiate the simulation’s parameters. Based on the observed consumer bidding strategies in real online auctions, three classes of bidding agents were developed and deployed. The validity of the simulation model is established and subsequently the simulation model is configured to change the values of key control factors, such as the bid increment. Our analysis indicates that the auctioneers are, most of the time, far away from the optimal choice of bid increment, resulting in substantial losses in a market with already tight margins. We discuss the challenges and key constructs of the model development. We also demonstrate the extended capabilities of the simulation tool by examining hybrid-bidding strategies derived as a combination of the original strategies used by online bidders.